In the study, published Thursday in Nature Climate Change, the authors argue that research aimed at assessing the humanity’s role in contributing to extreme weather events such as heatwaves and floods may be underestimating the contribution, according to the Wall Street Journal.
Interestingly, the study was prompted, at least in part, by President Donald Trump’s Tweet back in January 2019 about how a cold day in one particular location disproves global warming.
But unlike the president, the researchers used statistical techniques and climate model simulations to show the relationship between spatial patterns of daily temperature and humidity, and key climate change metrics such as annual global mean temperature or Earth’s energy imbalance.
They were able to show that spatial patterns of global temperature and humidity are, indeed, distinguishable from natural variability, and have a human “fingerprint” to them.
They went one step further, concluding that we can predict the long-term climate trend in global average temperature if we know a single day’s weather information worldwide. This fingerprint of climate change can be detected from any single day in the observed global record since early 2012, and since 1999 on the basis of a year of data.
Study co-author Reto Knutti of ETH Zurich, says this changes what we have been saying about the connection between weather and climate. “We’ve always said when you look at weather that’s not the same as climate,” he said. “That’s still true locally if you are in one particular place and you only know the weather right now, right here, there isn’t much you can say.”
Knutti points out that on a global scale, this no longer applies. “Global mean temperature on a single day is already quite a bit shifted. You can see this human fingerprint in any single moment. Weather is climate change if you look over the whole globe,” he said.
To come up with their conclusions, the authors, from research institutions in Switzerland and Norway, used machine learning to estimate how the patterns of temperature and moisture at daily, monthly and annual time scales relate to two important climate change metrics: global average surface temperatures and the energy imbalance of the planet.
The global increase in greenhouse gasses (GHG) in the atmosphere allows the Earth to hold in more of the sun’s energy, creating an energy surplus. The expected changes in climate are based on our understanding of how greenhouse gases trap heat. Both this fundamental understanding of the physics of greenhouse gases and fingerprint studies show that natural causes alone are inadequate to explain the recent observed changes in climate.
Using machine-learning techniques, the researchers were able to detect the human “fingerprint” based on relationships between weather and global warming metrics. They then compared that data with historical weather data.
What was particularly disturbing was the finding that the global warming fingerprint remained present even when the signal from the global average temperature trend was removed. Michael Wehner of Lawrence Berkeley National Laboratory, an outside expert not associated with the study called this insight ” profoundly disturbing.”
“This … is telling us that anthropogenic climate change has become so large that it exceeds even daily weather variability at the global scale,” Wehner said in an email. “This is disturbing as the Earth is on track for significantly more warming in even the most optimistic future scenarios.”